A new lung nodule simulation model was designed to create and insert synthetic solid lung nodules, with shapes
and density similar to real nodules, into normal MDCT chest exams. The nodule simulation model was validated
both subjectively by human experts and quantitatively by comparing density attenuation profiles of simulated
nodules with real nodules. These validation studies demonstrated a high level of similarity between the synthetic
nodules and real nodules. This nodule simulation model was used to create objective test databases for use in
evaluating lung nodule growth measurement of a CAD system. The performance evaluation studies demonstrated
a high level of accuracy for the automatic growth measurement tool, while the error margin for the growth
measurement increased with nodule size decreasing. The experiments also showed the volume/growth estimation
errors for low dose scans were comparable to the ones for the normal dose scans, thus demonstrated a robust
performance across different dosages.
A new lung nodule simulation model was designed to create and insert synthetic solid lung nodules, with shapes
and density similar to real nodules, into normal MDCT chest exams. Nodule shapes were modeled using linearly
deformed superquadrics with added randomly generated high dimensional deformations. Nodule density statistics
and attenuation profiles were extracted from a group of real nodule samples, by dissecting each real nodule
digitally layer by layer from the border to the core. A nodule created with modeled shape and density was
inserted into real CT images by creating volume average layers using weighted averaging between nodule density
and background density for each voxel. The nodule simulation model was validated both subjectively by human
experts and quantitatively by comparing density attenuation profiles of simulated nodules with real nodules.
These validation studies demonstrated a high level of similarity between the synthetic nodules and real nodules.
This nodule simulation model was used to create objective test databases for use in evaluating a CAD system. The
evaluation study showed that the CAD system was accurate in detection and volume measurement for isolated
nodules, and also performed relatively well for juxta-vascular nodules. The CAD system also demonstrated
stable performances for different dosages.
A novel method called local shape controlled voting has been developed for spherical object detection in 3D voxel
images. By combining local shape properties into the global tracking procedure of normal overlap, the proposed
method solved the ambiguities of normal overlap between a small size sphere and a possible large size cylinder,
as the normal overlap technique can only measures the 'density' of normal overlapping, while how the normal
vectors are distributed in 3D is not discovered. The proposed method was applied to computer aided detection
of small size pulmonary nodules based on helical CT images. Experiments showed that this method attained a
better performance compared to the original normal overlap technique.
The purpose of this study is to develop a computer-aided diagnosis (CAD) system to detect small-sized (from 2mm to 10mm) pulmonary nodules in high resolution helical CT scans. A new CAD system is proposed to locate both juxtapleural nodules and non-pleural nodules. Isotropic resampling and lung segmentation are performed as preprocessing steps. Morphological closing was utilized to smooth the lung contours to include the indented possible juxtapleural locations, thresholding and 3D component analysis were used to obtain 3D volumetric nodule candidates; furthermore, gray level and geometric features were extracted, and analyzed using linear discriminant analysis (LDA) classifier. Leave one case out method was used to evaluate the LDA. To deal with non-pleural nodules, a discrete-time cellular neural network (DTCNN) based on local shape features was developed. This scheme employed the local shape property to perform voxel classification. The shape index feature successfully captured the local shape difference between nodules and non-nodules, especially vessels. To tailor it for lung nodule detection, this DTCNN was trained using genetic algorithms (GAs) to derive the shape index variation pattern of nodules. Nonoverlapping training and testing sets were utilized in the non-pleural nodule detection. 19 clinical thoracic CT cases involving a total of 4838 sectional images were used in this work. The juxtapleural nodule detection method was able to obtain sensitivity 81.25% with an average of 8.29 FPs per case. The non-pleural nodule finding scheme attained sensitivity of 83.9% with an average 3.47 FPs/case. Combining the two subsystems together, an overall performance of 82.98% sensitivity with 11.76 FPs/case can be obtained.
The purpose of this study is to develop a computer-aided diagnosis (CAD) system to segment small size non-isolated pulmonary nodules in high resolution helical CT scans. A new automated method of segmenting juxtapleural nodules was proposed, in which a quadric surface fitting procedure was used to create a boundary between a juxtapleural nodule and its neighboring pleural surface. Experiments on some real CT nodule data showed that this method was able to yield results that reflect the local shape of the pleural surface. Additionally, a scheme based on parametrically deformable geometric model was developed to deal with the problem of segmenting nodules attached to vessels. A vessel segment connected to a nodule was modeled using superquadrics with parametric deformations. The boundary between a vascularized nodule and the attached vessels can be recovered by finding the deformed superquadrics which approximates the vessels. Gradient descent scheme was utilized to optimize the parameters of the superquadrics. Simple experiments on synthetic data showed this scheme is promising.
The purpose of this study is to develop a computer-aided diagnosis (CAD) system to detect small-sized (from 2mm to 10mm) non-pleural pulmonary nodules in high resolution helical CT scans. A new 3D automated scheme using cellular neural networks is presented. Different from most previous methods, this scheme employed the local shape property to perform voxel classification. The shape index feature successfully captured the local shape difference between nodules and non-nodules, especially vessels. A 3D discrete-time cellular neural network (DTCNN) was constructed to give a reliable voxel classification by collecting information in a neighborhood. To tailor it for lung nodule detection, this DTCNN was trained using genetic algorithms (GAs) to derive the shape index variation pattern of nodules. 19 clinical thoracic CT cases involving a total of 4838 sectional images were used in this work, with 2 scans forming the training set, and the remaining 17 cases being the testing set. The evaluation was composed of two stages. During the first stage, a pulmonologist and our CAD system independently detected nodules in the testing set. Then, the suspected nodule areas located by the computer were reviewed by the pulmonologist to confirm nodules missed by the human in the first review. There were 32 true nodules detected by the computer but missed by the pulmonologist in the first review, in which 30 non-juxtapleural nodules were found. Considering the nodules detected by the pulmonologist during the first and second reviews as the truth, 52 of 62 non-pleural nodules were detected by the CAD system (sensitivity being 83.9%), with the number of false positives being 3.47 per case.
KEYWORDS: Gaussian filters, 3D image processing, Image processing, Image segmentation, Image filtering, Medical imaging, 3D modeling, Linear filtering, Convolution, Analytical research
This work investigates curvature estimation of level surfaces in 3D voxel images. We discuss features of a widely used curvature computation scheme developed by Thirion. It is shown that the locality of the implicit function theorem destroys the smoothness of the reference unit normal; the specialty of Monge patch restricts the derived curvature formulas. By explicitly choosing the normalized gradient of the level function as the reference unit normal, a curvature computation scheme is developed and reported here, in which consistent geometrical meaning of curvature sign is maintained. In order to estimate the curvature efficiently for arbitrary discrete surfaces, a scheme based on distance mapping is proposed. This method was tested on simple 2D objects. It was shown that our method gives accurate curvature estimation similar to Gaussian filtering method, but with much less computational cost.
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